high performance computing on graphics processing units: hgpu.org

hgpu.org » Linear Algebra

Fast Radix Sort for Sparse Linear Algebra on GPU

Lukas Polok, Viorela Ila, Pavel Smrz

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Tags: Algorithms, Computer science, CUDA, Factorization, Linear Algebra, Matrix multiplication, nVidia, nVidia GeForce GTX 680, nVidia GeForce GTX 780, OpenCL, Sorting, Sparse matrix

March 18, 2015 by hgpu

Comparison of SPMV performance on matrices with different matrix format using CUSP, cuSPARSE and ViennaCL

Ang Li, Hammad Mazhar, Radu Serban, Dan Negrut

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Tags: Computer science, CUDA, Linear Algebra, nVidia, OpenCL, Performance, Python, Sparse matrix, Tesla K20

February 22, 2015 by hgpu

Reproducible Triangular Solvers for High-Performance Computing

Roman Iakymchuk, David Defour, Sylvain Collange, Stef Graillat

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Tags: Algorithms, Computer science, Linear Algebra, nVidia, nVidia Quadro K5000, OpenCL, Package, Tesla K20

February 19, 2015 by hgpu

Sparse Matrix-Vector Multiplication on GPU

Arash Ashari

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Tags: Algorithms, Computer science, CUDA, Linear Algebra, Machine learning, nVidia, nVidia GeForce GTX 580, nVidia GeForce GTX Titan, Sparse matrix, Tesla K10, Thesis

February 9, 2015 by hgpu

Accelerating Polynomial Homotopy Continuation on a Graphics Processing Unit with Double Double and Quad Double Arithmetic

Jan Verschelde, Xiangcheng Yu

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Tags: Algorithms, Computer science, CUDA, Extended precision, Linear Algebra, Mathematical Software, Numerical Analysis, nVidia, Package, Tesla K20

January 28, 2015 by hgpu

Subdivision Surface Evaluation as Sparse Matrix-Vector Multiplication

Michael Driscoll

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Tags: 3D Graphics and Realism, Computer science, CUDA, Linear Algebra, Matrix multiplication, nVidia, nVidia GeForce GTX 480, OpenCL, Package, Sparse matrix

January 5, 2015 by hgpu

Hierarchical DAG Scheduling for Hybrid Distributed Systems

Wei Wu, Aurelien Bouteiller, George Bosilca, Mathieu Faverge, Jack Dongarra

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Tags: Algorithms, Computer science, CUDA, Factorization, Linear Algebra, nVidia, Tesla K40, Tesla M2090

January 5, 2015 by hgpu

Towards an automatic generation of dense linear algebra solvers on parallel architectures

Marc Baboulin, Joel Falcou, Ian Masliah

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Tags: Computer science, CUDA, Linear Algebra, Mixed precision, nVidia, Tesla C2075

December 20, 2014 by hgpu

Bamboo: Automatic Translation of MPI Source into a Latency-Tolerant Form

Nhat Tan Nguyen Thanh

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Tags: Computer science, CUDA, Linear Algebra, MPI, nVidia, Package, Tesla K20, Thesis

December 15, 2014 by hgpu

Acceleration of Hessenberg Reduction for Nonsymmetric Matrix

Hesamaldin Nekouei

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Tags: Algorithms, Computer science, CUDA, FFT, Linear Algebra, nVidia, nVidia GeForce GT 635 M, Thesis

December 14, 2014 by hgpu

Implementing Level-3 BLAS Routines in OpenCL on Different Processing Units

Kazuya Matsumoto, Naohito Nakasato, Stanislav Sedukhin

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Tags: ATI, ATI Radeon HD 7970, BLAS, Computer science, Intel Xeon Phi, Linear Algebra, Matrix multiplication, nVidia, nVidia GeForce GTX Titan, OpenCL

October 29, 2014 by hgpu

Pipelined Iterative Solvers with Kernel Fusion for Graphics Processing Units

Karl Rupp, Josef Weinbub, Ansgar Jungel, Tibor Grasser

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Tags: AMD FirePro W9000, AMD FirePro W9100, ATI, Computer science, Conjugate gradient solver, CUDA, Linear Algebra, nVidia, OpenCL, Tesla C2050, Tesla K20

October 16, 2014 by hgpu

CuPBoP-AMD: Extending CUDA to AMD Platforms

CuPBoP: Making CUDA a Portable Language

Adopter: Automated Deep Learning Optimization via DSL-based Source Code Transformation

Automated Deep Learning Optimization via DSL-Based Source Code Transformation

ROCm's implementation of Gromacs

GROMACS on AMD GPU-Based HPC Platforms: Using SYCL for Performance and Portability

Code examples for paper on SYCL backend of Kokkos - IWOCL 2024

Experiences with implementing Kokkos’ SYCL backend

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

SimSYCL: A SYCL Implementation Targeting Development, Debugging, Simulation and Conformance

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

gpu_tracker: Python package for tracking and profiling GPU utilization in both desktop and high-performance computing environments

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

94% on CIFAR-10 in 3.29 Seconds on a Single GPU

See all packages

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high performance computing on graphics processing units: hgpu.org

Fast Radix Sort for Sparse Linear Algebra on GPU

Comparison of SPMV performance on matrices with different matrix format using CUSP, cuSPARSE and ViennaCL

Reproducible Triangular Solvers for High-Performance Computing

Sparse Matrix-Vector Multiplication on GPU

Accelerating Polynomial Homotopy Continuation on a Graphics Processing Unit with Double Double and Quad Double Arithmetic

Subdivision Surface Evaluation as Sparse Matrix-Vector Multiplication

Hierarchical DAG Scheduling for Hybrid Distributed Systems

Towards an automatic generation of dense linear algebra solvers on parallel architectures

Bamboo: Automatic Translation of MPI Source into a Latency-Tolerant Form

Acceleration of Hessenberg Reduction for Nonsymmetric Matrix

Implementing Level-3 BLAS Routines in OpenCL on Different Processing Units

Pipelined Iterative Solvers with Kernel Fusion for Graphics Processing Units

Recent source codes

CuPBoP-AMD: Extending CUDA to AMD Platforms

Adopter: Automated Deep Learning Optimization via DSL-based Source Code Transformation

ROCm's implementation of Gromacs

Code examples for paper on SYCL backend of Kokkos - IWOCL 2024

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

GPU plugin for PySCF

QArray

Celerity: High-level C++ for Accelerator Clusters

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

Most viewed papers (last 30 days)